In the fields of medical imaging and radiology, various techniques may be employed for creating images of an anatomical region of the human body. For example, in mammography, the breast is often imaged at two fixed angles using x-rays. Physicians may review two-dimensional (2-D) or planar x-ray images of the breast (i.e., mammograms) to uncover and diagnose disease-like conditions, such as breast cancer.
Numerous medical imaging procedures now employ systems and techniques that create three-dimensional (3-D) or volumetric imagery of the human body. For example, significant attention has been given to tomographic imaging techniques. One such example is digital breast tomosynthesis (DBT), a relatively new imaging procedure in which systems image a breast by moving a source and exposing the breast to radiation from a plurality of angles, thus acquiring high resolution, planar images (i.e., “direct projections”) at different angles. For example, a DBT system may acquire 10 direct projection images in which the source moves in such a way as to change the imaging angle by a total angle of 40 degrees. From these direct projections, computer software can be used to construct a 3-D volume of the breast (i.e., a “reconstructed volume”). A reconstructed volume may be used to derive a series of 40-60 individual images called slices that are oriented parallel to a single plane of the imaged object (i.e., the “reconstruction plane”). The computer software may reconstruct each slice at a different depth and may use different thicknesses, allowing physicians to visualize the breast and information of interest at various depths of field that were previously unavailable with traditional 2-D imaging systems and procedures.
In mammography, a physician typically may review four images to diagnose a patient: a cranial-caudal (CC) image and a medial-lateral oblique (MLO) image of each of the right and left breasts. In contrast, in tomographic imaging such as DBT, a physician may review any of the direct projections and/or reconstructed volume slices to diagnose a patient. For example, the physician may use an input device in combination with a computer system and graphical user interface (GUI) to “scroll” through slice images displayed on the GUI, so as to simulate moving through the breast perpendicular to the reconstruction plane. Any of these images or combination of images may depict information of interest in a way that allows a physician to detect and diagnose a potential disease-like condition. Thus, while tomographic imaging may allow a physician to improve the overall quality of care to a patient over traditional mammographic imaging, the substantial amount of image data available may have a negative impact on the physician's workload and interpretation time.
Computer-aided detection (CAD) is one solution to help a physician to overcome problems such as workload and interpretation time. Using sophisticated computer algorithms based on image processing and pattern recognition disciplines, CAD systems may detect and present information (e.g., lesions) in medical imagery that may be of interest to a reviewing physician. CAD has enjoyed widespread success in its application to mammographic medical imaging, as it has been shown to improve patient care, reduce human workload, and reduce human error associated with fatigue or variability between observers. More recently, CAD has been proposed and developed to assist physicians moving to tomographic imaging procedures.
Given the increase in the amount of image data acquired by tomographic imaging techniques, several different approaches are feasible in which a computer system can perform CAD and present information of interest to a physician.
CAD may be performed on direct projection images acquired by the system. For example, each individual direct projection may be analyzed. However, direct projections may be noisy and, like mammography, may have a very limited depth of field. Thus, if CAD is performed on the direct projections, important regions may be obscured by other uninteresting tissue in the direct projections and therefore, not detected by CAD.
Alternatively, CAD may be performed on the voxels of the entire reconstructed volume. However, the spatial distortion and noise characteristics of the reconstructed volumes may be complicated, requiring more sophisticated and/or customized algorithms, computational power, computational storage, and computational time. Thus, if CAD is performed on the entire reconstructed volume, the workflow of a physician may be negatively impacted by the speed of such a system. Physicians may require CAD to assist them in reviewing and diagnosing the imagery of numerous patients each day.
Alternatively, CAD may be performed on the individual slices derived from a reconstructed tomographic volume. An overview of such a technique, as well as an overview of the aforementioned techniques, may be found in U.S. Pat. No. 6,748,044, “Computer assisted analysis of tomographic mammography data,” assigned to GE Medical Systems Global Technology Company, LLC. While each individual slice may have information about lesions or other structures of interest at a range of depths throughout the object, a lesion or other structure of interest may be spread across a plurality of slices. This may be particularly true if the slices of the tomographic volume are reconstructed with thicknesses that are less than the expected size of the lesion or other structure of interest. Resolving this information may be problematic for a computer system, leading to false detections, missed detections, and/or poorly-represented detections.
It is therefore an object of this disclosure to present methods and systems to automatically detect and present information about lesions and other structures of interest in tomographic imagery of the breast in a manner that is advantageous for use in a clinical setting, both in terms of computational speed and detection accuracy.